Graphs in Artificial Intelligence
Neural Networks

GAIN - Project

Welcome to the homepage of the junior research group "Graphs in Artificial Intelligence and Neural Networks" (GAIN)!

We are working in one of the youngest and fastest growing research areas of Machine Learning, which is aiming to continue the success of Deep Learning to data represented by graphs.

Currently, our focus is on the dynamics and explainability of Graph Neural Networks (GNNs) as well as their applications to the power grid and printed circuit boards.
By dynamic, we mean to develop GNN-algorithms that can deal with changes in the topology of a graph or changing graph attributes. In the simplest case that might be new appearing nodes or a change in an attribute describing a node.
At the same time, we want our algorithms to be explainable, which means that either inherently or post-hoc our algorithms should give a reason for their predictions.

We chose this focus, because dynamics will enable the use of GNN-based methods in supply structure networks to handle the increasing amount of renewable energies, while explainability will make their application more likely.

The GAIN-group originates from the ongoing GAIN-project, since 2024 we are also working on the projects GNN4GC (Graph Neural Networks for Grid Control) and GraphPCBS (Optimization of Printed Circuit Boards with Graph Neural Networks).

We are situated at the department ofIntelligent Embedded Systemsat the university of Kassel and project partners with theFraunhofer Institute for Energy Economics and Energy Systems Technology (Fraunhofer IEE) and the smart electronics-engineering company CELUS.

Enjoy browsing our pages and feel free to contact us via the addresses provided in thecontactsection. You can also find our PGP-keys there. Have fun!


We are funded by the Federal Ministry of Education and Research Germany (BMBF) under the following funding codes:
01IS20047A, according to the 'Policy for the funding of female junior researchers in Artificial Intelligence',
16ME0877, according to the KMU-innovativ' guideline.
We are funded by the Federal Ministry for Economic Affairs and Climate Action(BMWK) under the following funding code:
020E-100626677, within the '7. Energieforschungsprogramm'.

The responsibility for the content of this website or of any publication lies with the author.